BenchMARL AI Agent

by facebookresearch• 678•• Scanned•Claim this listing•Request removal
Learning Resourcesbenchmarkmachine learningmarlmulti agentmulti agent reinforcement learning

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.

BenchMARL is an open-source ai agent by facebookresearch, written mainly in Python, with 678 GitHub stars and the MIT license. Its code was last updated Feb 7, 2026.

Scan summary: scanned Nov 5, 2026 at commit 65d649d. Seen able to run dynamic code. Scan policy

Scanned
Full scan at 65d649d · Nov 5, 2026
Scanned Builds
Static code scanwarn
Secrets scanpass
Dependency audit6 known vulnerabilitieswarn
Manifestnonewarn
Buildpass
Boot & handshaken/a
Network egressn/a
Reproducible (×3)n/a

Findings (11)

  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1044
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1045
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1046
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1047
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1048
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1049
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1050
  • medium
    Deserializes data with pickle/marshal, which can execute code.
    benchmarl/experiment/experiment.py:1051
  • medium
    orjson@3.9.9: 2 known vulnerabilities
    PYSEC-2024-40, PYSEC-2026-107
    docs/requirements.txt
  • medium
    torch@2.9.1: 4 known vulnerabilities
    PYSEC-2026-139, PYSEC-2026-2286, GHSA-qfhq-4f3w-5fph, GHSA-rrmf-rvhw-rf47
    docs/requirements.txt
  • low
    No manifest declaring network access and environment variables (agent.json)

Network activity

During install
files.pythonhosted.orgpypi.org
While running
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